报告题目：Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting

报 告 人：谌自奇 教授 华东师范大学

报告时间：2026年6月24日15:30 - 16:30

报告地点：伍卓群楼第二报告厅

校内联系人：朱复康 [email protected]

报告摘要：

    Probabilistic forecasting of multivariate time series is challenging due to nonstationarity, inter-variable dependencies, and distribution shifts. While recent diffusion and flow matching models have shown promise, they often ignore informative priors such as conditional means and covariances. In this work, we propose Conditionally Whitened Generative Models (CW-Gen), a framework that incorporates prior information through conditional whitening. Theoretically, we establish sufficient conditions under which replacing the traditional terminal distribution of diffusion models, namely the standard multivariate normal, with a multivariate normal distribution parameterized by estimators of the conditional mean and covariance improves sample quality. Guided by this analysis, we design a novel Joint Mean-Covariance Estimator (JMCE) that simultaneously learns the conditional mean and sliding-window covariance. Building on JMCE, we introduce Conditionally Whitened Diffusion Models (CW-Diff) and extend them to Conditionally Whitened Flow Matching (CW-Flow). Experiments on five real-world datasets with six state-of-the-art generative models demonstrate that CW-Gen consistently enhances predictive performance, capturing non-stationary dynamics and inter-variable correlations more effectively than prior-free approaches. Empirical results further demonstrate that CW-Gen can effectively mitigate the effects of distribution shift.

报告人简介：

    谌自奇，华东师范大学研究员，紫江青年学者，博士生导师。博士毕业于东北师范大学，曾在美国安德森癌症研究中心生物统计系从事博士后研究工作。主要研究方向包括因果结构学习、生成模型、数据融合、图神经网络等。主持国家自然科学基金面上项目2项、国家自然科学基金重点项目（子课题）1项、国家自然科学基金青年项目1项、上海市自然科学基金项目1项；并作为骨干成员参与国家重点研发计划及上海市"科技创新行动计划"基础研究领域应用数学重点项目等。相关研究成果发表于Journal of the American Statistical Association、Biometrics、Statistica Sinica、Scandinavian Journal of Statistics等国际权威统计期刊，以及NeurIPS、ICLR、KDD、AAAI等人工智能顶级会议。现任统计期刊Statistical Theory and Related Fields副主编，并担任该刊"人工智能与机器学习"专刊客座主编。